OpenRaiser/Intern-Atlas huggingface dataset:
Towards Software-Defined Data Protection: GDPR Compliance at the Storage Layer is Within Reach
Venue: arXiv cs.CR 2020
Year: 2020

Enforcing data protection and privacy rules within large data processing applications is becoming increasingly important, especially in the light of GDPR and similar regulatory frameworks. Most modern data processing happens on top of a distributed storage layer, and securing this layer against accidental or malicious misuse is crucial to ensuring global privacy guarantees. However, the performance overhead and the additional complexity for this is often assumed to be significant -- in this work we describe a path forward that tackles both challenges. We propose "Software-Defined Data Protection" (SDP), an adoption of the "Software-Defined Storage" approach to non-performance aspects: a trusted controller translates company and application-specific policies to a set of rules deployed on the storage nodes. These, in turn, apply the rules at line-rate but do not take any decisions on their own. Such an approach decouples often changing policies from request-level enforcement and allows storage nodes to implement the latter more efficiently. Even though in-storage processing brings challenges, mainly because it can jeopardize line-rate processing, we argue that today's Smart Storage solutions can already implement the required functionality, thanks to the separation of concerns introduced by SDP. We highlight the challenges that remain, especially that of trusting the storage nodes. These need to be tackled before we can reach widespread adoption in cloud environments.
Predicting the Citations of Scholarly Paper
Venue: arXiv cs.DL 2020
Year: 2020

Citation prediction of scholarly papers is of great significance in guiding funding allocations, recruitment decisions, and rewards. However, little is known about how citation patterns evolve over time. By exploring the inherent involution property in scholarly paper citation, we introduce the Paper Potential Index (PPI) model based on four factors: inherent quality of scholarly paper, scholarly paper impact decaying over time, early citations, and early citers' impact. In addition, by analyzing factors that drive citation growth, we propose a multi-feature model for impact prediction. Experimental results demonstrate that the two models improve the accuracy in predicting scholarly paper citations. Compared to the multi-feature model, the PPI model yields superior predictive performance in terms of range-normalized RMSE. The PPI model better interprets the changes in citation, without the need to adjust parameters. Compared to the PPI model, the multi-feature model performs better prediction in terms of Mean Absolute Percentage Error and Accuracy; however, their predictive performance is more dependent on the parameter adjustment.
Select Good Regions for Deblurring based on Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

The goal of blind image deblurring is to recover sharp image from one input blurred image with an unknown blur kernel. Most of image deblurring approaches focus on developing image priors, however, there is not enough attention to the influence of image details and structures on the blur kernel estimation. What is the useful image structure and how to choose a good deblurring region? In this work, we propose a deep neural network model method for selecting good regions to estimate blur kernel. First we construct image patches with labels and train a deep neural networks, then the learned model is applied to determine which region of the image is most suitable to deblur. Experimental results illustrate that the proposed approach is effective, and could be able to select good regions for image deblurring.
Sampling Based Approximate Skyline Calculation on Big Data
Venue: arXiv cs.DB 2020
Year: 2020

The existing algorithms for processing skyline queries cannot adapt to big data. This paper proposes two approximate skyline algorithms based on sampling. The first algorithm obtains a fixed size sample and computes the approximate skyline on the sample. The error of the first algorithm is relatively small in most cases, and is almost independent of the input relation size. The second algorithm returns an $(ε,δ)$-approximation for the exact skyline. The size of sample required by the second algorithm can be regarded as a constant relative to the input relation size, so is the running time. Experiments verify the error analysis of the first algorithm and show that the second algorithm is much faster than the existing skyline algorithms.
Facial Expression Retargeting from Human to Avatar Made Easy
Venue: arXiv cs.CV 2020
Year: 2020

Facial expression retargeting from humans to virtual characters is a useful technique in computer graphics and animation. Traditional methods use markers or blendshapes to construct a mapping between the human and avatar faces. However, these approaches require a tedious 3D modeling process, and the performance relies on the modelers' experience. In this paper, we propose a brand-new solution to this cross-domain expression transfer problem via nonlinear expression embedding and expression domain translation. We first build low-dimensional latent spaces for the human and avatar facial expressions with variational autoencoder. Then we construct correspondences between the two latent spaces guided by geometric and perceptual constraints. Specifically, we design geometric correspondences to reflect geometric matching and utilize a triplet data structure to express users' perceptual preference of avatar expressions. A user-friendly method is proposed to automatically generate triplets for a system allowing users to easily and efficiently annotate the correspondences. Using both geometric and perceptual correspondences, we trained a network for expression domain translation from human to avatar. Extensive experimental results and user studies demonstrate that even nonprofessional users can apply our method to generate high-quality facial expression retargeting results with less time and effort.
Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?
Venue: arXiv cs.CV 2020
Year: 2020

Detecting Graphical User Interface (GUI) elements in GUI images is a domain-specific object detection task. It supports many software engineering tasks, such as GUI animation and testing, GUI search and code generation. Existing studies for GUI element detection directly borrow the mature methods from computer vision (CV) domain, including old fashioned ones that rely on traditional image processing features (e.g., canny edge, contours), and deep learning models that learn to detect from large-scale GUI data. Unfortunately, these CV methods are not originally designed with the awareness of the unique characteristics of GUIs and GUI elements and the high localization accuracy of the GUI element detection task. We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods. This study not only sheds the light on the technical challenges to be addressed but also informs the design of new GUI element detection methods. We accordingly design a new GUI-specific old-fashioned method for non-text GUI element detection which adopts a novel top-down coarse-to-fine strategy, and incorporate it with the mature deep learning model for GUI text detection.Our evaluation on 25,000 GUI images shows that our method significantly advances the start-of-the-art performance in GUI element detection.
The Italian bondage and reinforcement numbers of digraphs
Venue: arXiv cs.DM 2020
Year: 2020

An \textit{Italian dominating function} on a digraph $D$ with vertex set $V(D)$ is defined as a function $f : V(D) \rightarrow \{0, 1, 2\}$ such that every vertex $v \in V(D)$ with $f(v) = 0$ has at least two in-neighbors assigned $1$ under $f$ or one in-neighbor $w$ with $f(w) = 2$. The \textit{weight} of an Italian dominating function $f$ is the value $ω(f) = f(V(D)) = \sum_{u \in V(D)} f(u)$. The \textit{Italian domination number} of a digraph $D$, denoted by $γ_I(D)$, is the minimum taken over the weights of all Italian dominating functions on $D$. The \textit{Italian bondage number} of a digraph $D$, denoted by $b_I(D)$, is the minimum number of arcs of $A(D)$ whose removal in $D$ results in a digraph $D'$ with $γ_I(D') > γ_I(D)$. The \textit{Italian reinforcement number} of a digraph $D$, denoted by $r_I(D)$, is the minimum number of extra arcs whose addition to $D$ results in a digraph $D'$ with $γ_I(D') < γ_I(D)$. In this paper, we initiate the study of Italian bondage and reinforcement numbers in digraphs and present some bounds for $b_I(D)$ and $r_I(D)$. We also determine the Italian bondage and reinforcement numbers of some classes of digraphs.
The network footprint of replication in popular DBMSs
Venue: arXiv cs.DB 2020
Year: 2020

Database replication is an important component of reliable, disaster tolerant and highly available distributed systems. However, data replication also causes communication and processing overhead. Quantification of these overheads is crucial in choosing a suitable DBMS form several available options and capacity planning. In this paper, we present results from a comparative empirical analysis of replication activities of three commonly used DBMSs - MySQL, PostgreSQL and Cassandra under text as well as image traffic. In our experiments, the total traffic with two replicas (which is the norm) was as much as $300$\% higher than the total traffic with no replica. Furthermore, activation of the compression option for replication traffic, built into MySQL, reduced the total network traffic by as much as $20$\%. We also found that average CPU utilization and memory utilization were not impacted by the number of replicas or the dataset.
RAF-AU Database: In-the-Wild Facial Expressions with Subjective Emotion Judgement and Objective AU Annotations
Venue: arXiv cs.CV 2020
Year: 2020

Much of the work on automatic facial expression recognition relies on databases containing a certain number of emotion classes and their exaggerated facial configurations (generally six prototypical facial expressions), based on Ekman's Basic Emotion Theory. However, recent studies have revealed that facial expressions in our human life can be blended with multiple basic emotions. And the emotion labels for these in-the-wild facial expressions cannot easily be annotated solely on pre-defined AU patterns. How to analyze the action units for such complex expressions is still an open question. To address this issue, we develop a RAF-AU database that employs a sign-based (i.e., AUs) and judgement-based (i.e., perceived emotion) approach to annotating blended facial expressions in the wild. We first reviewed the annotation methods in existing databases and identified crowdsourcing as a promising strategy for labeling in-the-wild facial expressions. Then, RAF-AU was finely annotated by experienced coders, on which we also conducted a preliminary investigation of which key AUs contribute most to a perceived emotion, and the relationship between AUs and facial expressions. Finally, we provided a baseline for AU recognition in RAF-AU using popular features and multi-label learning methods.
OCoR: An Overlapping-Aware Code Retriever
Venue: arXiv cs.CL 2020
Year: 2020

Code retrieval helps developers reuse the code snippet in the open-source projects. Given a natural language description, code retrieval aims to search for the most relevant code among a set of code. Existing state-of-the-art approaches apply neural networks to code retrieval. However, these approaches still fail to capture an important feature: overlaps. The overlaps between different names used by different people indicate that two different names may be potentially related (e.g., "message" and "msg"), and the overlaps between identifiers in code and words in natural language descriptions indicate that the code snippet and the description may potentially be related. To address these problems, we propose a novel neural architecture named OCoR, where we introduce two specifically-designed components to capture overlaps: the first embeds identifiers by character to capture the overlaps between identifiers, and the second introduces a novel overlap matrix to represent the degrees of overlaps between each natural language word and each identifier. The evaluation was conducted on two established datasets. The experimental results show that OCoR significantly outperforms the existing state-of-the-art approaches and achieves 13.1% to 22.3% improvements. Moreover, we also conducted several in-depth experiments to help understand the performance of different components in OCoR.
A Zero-Shot Sketch-based Inter-Modal Object Retrieval Scheme for Remote Sensing Images
Venue: arXiv cs.CV 2020
Year: 2020

Conventional existing retrieval methods in remote sensing (RS) are often based on a uni-modal data retrieval framework. In this work, we propose a novel inter-modal triplet-based zero-shot retrieval scheme utilizing a sketch-based representation of RS data. The proposed scheme performs efficiently even when the sketch representations are marginally prototypical of the image. We conducted experiments on a new bi-modal image-sketch dataset called Earth on Canvas (EoC) conceived during this study. We perform a thorough bench-marking of this dataset and demonstrate that the proposed network outperforms other state-of-the-art methods for zero-shot sketch-based retrieval framework in remote sensing.
PAM:Point-wise Attention Module for 6D Object Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

6D pose estimation refers to object recognition and estimation of 3D rotation and 3D translation. The key technology for estimating 6D pose is to estimate pose by extracting enough features to find pose in any environment. Previous methods utilized depth information in the refinement process or were designed as a heterogeneous architecture for each data space to extract feature. However, these methods are limited in that they cannot extract sufficient feature. Therefore, this paper proposes a Point Attention Module that can efficiently extract powerful feature from RGB-D. In our Module, attention map is formed through a Geometric Attention Path(GAP) and Channel Attention Path(CAP). In GAP, it is designed to pay attention to important information in geometric information, and CAP is designed to pay attention to important information in Channel information. We show that the attention module efficiently creates feature representations without significantly increasing computational complexity. Experimental results show that the proposed method outperforms the existing methods in benchmarks, YCB Video and LineMod. In addition, the attention module was applied to the classification task, and it was confirmed that the performance significantly improved compared to the existing model.
Secure IoT Data Analytics in Cloud via Intel SGX
Venue: arXiv cs.CR 2020
Year: 2020

The growing adoption of IoT devices in our daily life is engendering a data deluge, mostly private information that needs careful maintenance and secure storage system to ensure data integrity and protection. Also, the prodigious IoT ecosystem has provided users with opportunities to automate systems by interconnecting their devices and other services with rule-based programs. The cloud services that are used to store and process sensitive IoT data turn out to be vulnerable to outside threats. Hence, sensitive IoT data and rule-based programs need to be protected against cyberattacks. To address this important challenge, in this paper, we propose a framework to maintain confidentiality and integrity of IoT data and rule-based program execution. We design the framework to preserve data privacy utilizing Trusted Execution Environment (TEE) such as Intel SGX, and end-to-end data encryption mechanism. We evaluate the framework by executing rule-based programs in the SGX securely with both simulated and real IoT device data.
Anomaly localization by modeling perceptual features
Venue: arXiv cs.CV 2020
Year: 2020

Although unsupervised generative modeling of an image dataset using a Variational AutoEncoder (VAE) has been used to detect anomalous images, or anomalous regions in images, recent works have shown that this method often identifies images or regions that do not concur with human perception, even questioning the usability of generative models for robust anomaly detection. Here, we argue that those issues can emerge from having a simplistic model of the anomaly distribution and we propose a new VAE-based model expressing a more complex anomaly model that is also closer to human perception. This Feature-Augmented VAE is trained by not only reconstructing the input image in pixel space, but also in several different feature spaces, which are computed by a convolutional neural network trained beforehand on a large image dataset. It achieves clear improvement over state-of-the-art methods on the MVTec anomaly detection and localization datasets.
Attention-based Fully Gated CNN-BGRU for Russian Handwritten Text
Venue: arXiv cs.CV 2020
Year: 2020

This research approaches the task of handwritten text with attention encoder-decoder networks that are trained on Kazakh and Russian language. We developed a novel deep neural network model based on Fully Gated CNN, supported by Multiple bidirectional GRU and Attention mechanisms to manipulate sophisticated features that achieve 0.045 Character Error Rate (CER), 0.192 Word Error Rate (WER) and 0.253 Sequence Error Rate (SER) for the first test dataset and 0.064 CER, 0.24 WER and 0.361 SER for the second test dataset. Also, we propose fully gated layers by taking the advantage of multiple the output feature from Tahn and input feature, this proposed work achieves better results and We experimented with our model on the Handwritten Kazakh & Russian Database (HKR). Our research is the first work on the HKR dataset and demonstrates state-of-the-art results to most of the other existing models.
Tight Bounds on Subexponential Time Approximation of Set Cover and Related Problems
Venue: arXiv cs.DS 2020
Year: 2020

We show that Set Cover on instances with $N$ elements cannot be approximated within $(1-γ)\ln N$-factor in time exp($N^{γ-δ})$, for any $0 < γ< 1$ and any $δ> 0$, assuming the Exponential Time Hypothesis. This essentially matches the best upper bound known by Cygan et al.\ (IPL, 2009) of $(1-γ)\ln N$-factor in time $exp(O(N^γ))$. The lower bound is obtained by extracting a standalone reduction from Label Cover to Set Cover from the work of Moshkovitz (Theory of Computing, 2015), and applying it to a different PCP theorem than done there. We also obtain a tighter lower bound when conditioning on the Projection Games Conjecture. We also treat three problems (Directed Steiner Tree, Submodular Cover, and Connected Polymatroid) that strictly generalize Set Cover. We give a $(1-γ)\ln N$-approximation algorithm for these problems that runs in $exp(\tilde{O}(N^γ))$ time, for any $1/2 \le γ< 1$.
Improving the Performance of Fine-Grain Image Classifiers via Generative Data Augmentation
Venue: arXiv cs.CV 2020
Year: 2020

Recent advances in machine learning (ML) and computer vision tools have enabled applications in a wide variety of arenas such as financial analytics, medical diagnostics, and even within the Department of Defense. However, their widespread implementation in real-world use cases poses several challenges: (1) many applications are highly specialized, and hence operate in a \emph{sparse data} domain; (2) ML tools are sensitive to their training sets and typically require cumbersome, labor-intensive data collection and data labelling processes; and (3) ML tools can be extremely "black box," offering users little to no insight into the decision-making process or how new data might affect prediction performance. To address these challenges, we have designed and developed Data Augmentation from Proficient Pre-Training of Robust Generative Adversarial Networks (DAPPER GAN), an ML analytics support tool that automatically generates novel views of training images in order to improve downstream classifier performance. DAPPER GAN leverages high-fidelity embeddings generated by a StyleGAN2 model (trained on the LSUN cars dataset) to create novel imagery for previously unseen classes. We experimentally evaluate this technique on the Stanford Cars dataset, demonstrating improved vehicle make and model classification accuracy and reduced requirements for real data using our GAN based data augmentation framework. The method's validity was supported through an analysis of classifier performance on both augmented and non-augmented datasets, achieving comparable or better accuracy with up to 30\% less real data across visually similar classes. To support this method, we developed a novel augmentation method that can manipulate semantically meaningful dimensions (e.g., orientation) of the target object in the embedding space.
User Popularity-based Packet Scheduling for Congestion Control in Ad-hoc Social Networks
Venue: arXiv cs.NI 2020
Year: 2020

Traditional ad-hoc network packet scheduling schemes cannot fulfill the requirements of proximity-based ad-hoc social networks (ASNETs) and they do not behave properly in congested environments. To address this issue, we propose a user popularity-based packet scheduling scheme for congestion control in ASNETs called Pop-aware. The proposed algorithm exploits social popularity of sender nodes to prioritize all incoming flows. Pop-aware also provides fairness of service received by each flow. We evaluate the performance of Pop-aware through a series of simulations. In comparison with some existing scheduling algorithms, Pop-aware performs better in terms of control overhead, total overhead, average throughput, packet loss rate, packet delivery rate and average delay.
Improving information retrieval from electronic health records using dynamic and multi-collaborative filtering
Venue: arXiv cs.IR 2020
Year: 2020

Due to the rapid growth of information available about individual patients, most physicians suffer from information overload when they review patient information in health information technology systems. In this manuscript, we present a novel hybrid dynamic and multi-collaborative filtering method to improve information retrieval from electronic health records. This method recommends relevant information from electronic health records for physicians during patient visits. It models information search dynamics using a Markov model. It also leverages the key idea of collaborative filtering, originating from Recommender Systems, to prioritize information based on various similarities among physicians, patients and information items. We tested this new method using real electronic health record data from the Indiana Network for Patient Care. Our experimental results demonstrated that for 46.7% of testing cases, this new method is able to correctly prioritize relevant information among top-5 recommendations that physicians are truly interested in.